Penerapan Algoritma Genetika pada Support Vector Machine Sebagai Pengoptimasi Parameter untuk Memprediksi Kesuburan

Penerapan Algoritma Genetika pada Support Vector Machine Sebagai Pengoptimasi Parameter untuk Memprediksi Kesuburan

• By Harafani, Hani; Maulana, Andry;
• Jurnal Nasional Tidak Terakreditasi
• 267
• 51-59

Fertility rates ini various countries have decreased. The result of the WHO study found 50% the causes of infertility were men caused by a decrease in the quality of semen. In this study, Genetic Algorithm and SVM Methods are used to predict the quality of semen in the Fertility dataset. Based on experiments with 10 iterations, the highest level of accuracy knowb is SVM+GA(dot kernel) of 89%, then SVM of 88%, followed by Decision Tree 84%, Neural Network 82%, and Naïve Bayes 82%. In Conclusion, GA is proven to increase the accuracy value of SVM  with kernel dot which shows a significant difference, although 2 kernel of SVM shows insignificant differences

• Katakunci GA, SVM, Fertility, Prediksi, Parameter
• URL http://ejournal.antarbangsa.ac.id/index.php/jti/article/view/303
• Penerbit Jurnal Teknik Informatika STMIK Antar Bangsa, Ciledug Tangerang

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